We argued that evolutionary design is different from the usual processes in which human designers or conventional CAD tools engage. One consequence is that it is possible to evolve designs that take unusual leverage from the physics of their medium of implementation. This can be done even if there is no tractable analytical model to predict how the overall behaviour will emerge from the interactions of the components. At least for small systems, it can also be done with little prior conception of what kinds of design might be appropriate or effective. These properties are alluring for contemporary single-electronics, and the experiments were encouraging.
A representation scheme was developed that allows more flexibility than a regular array, yet maintains adjacency of interacting components. Some freedom was available to explore circuits of different sizes, and with their output in different positions. The bias voltage and the signal levels could co-evolve with the circuit structure. A method was found for the evolution of circuits to perform at nonzero temperature, which also appears to lessen the impact of multiple-order tunnelling events, which we would rather not have to simulate during evolution. The resulting circuit does not serve as a good NOR gate, but does exhibit the ability of evolutionary techniques to navigate into intriguing unchartered territories of design.
Some important issues were not part of this first study. Perhaps the possibility of signal representation schemes other than voltage levels would be fruitful (e.g. [15]), and the issues surrounding the composition of evolved primitives into larger systems are worth closer attention. We ignored the effects of background charge and component tolerances, drift, and control, although parallel work has addressed the challenge of robustness in evolutionary microelectronics design [22].
It may be that a better NOR gate was not obtained because the fitness evaluations were quite noisy (visible in Fig. 7). In common with many of the future demands mentioned above, perhaps more computationally expensive fitness evaluations will be required. The set of experiments reported here took about 3 weeks on a dual-processor 466MHz PC. The computational demands are not necessarily terminal, as evolutionary algorithms parallelize very well to loosely-coupled MIMD parallel machines, such as cost-effective Beowulf-style clusters [4].
Nanoelectronics design seeks to employ subtle physics to do useful work. Natural evolution has done this in biology, and so can evolutionary algorithms in artificial media. Our experiments tentatively suggest that evolutionary methods may be a useful exploratory tool into novel kinds of design that may help to make such new technologies viable.